- numGibbs no longer part of EntityParameter

- refactored forward and reconstruct
- conditionally use optimizer for training

src/rbm/stack_deep.py

@@ -1,8 +1,10 @@
 from .status import Status
-from .train import train
+from .train import train, Optimizer
 from .stack import Stack, StackType
 from .matrix import Mat, np
 
+USE_OPTIMIZER = False
+
 class StackDeep(Stack):
 	def __init__(self, name: str, work_dir: str = '.'):
 		Stack.__init__(self, StackType.Deep, name, work_dir)
@@ -12,7 +14,7 @@ class StackDeep(Stack):
 		for index, layer in enumerate(self.layers):
 			if index == from_layer_id:
 				break
-			_batch = layer.entity.v_to_ph(_batch)
+			_batch = layer.entity.forward(_batch)
 
 		return _batch
 
@@ -20,8 +22,12 @@ class StackDeep(Stack):
 		_batch = np.copy(batch)
 		for index, layer in enumerate(self.layers):
 			print(f"Train layer {index} for {layer.training_params.num_epochs} epochs")
-			_batch = self.batch_from(batch, index)
-			train(layer.entity, _batch, layer.training_params, status=status)
+			if USE_OPTIMIZER:
+				optim = Optimizer(layer.entity, layer.training_params)
+				_batch = optim(_batch, status=status)
+			else:
+				_batch = self.batch_from(batch, index)
+				train(layer.entity, _batch, layer.training_params, status=status)
 
 
 	def pass_up(self, visible: Mat, from_layer_id: int = 0):